AbstentionBench - answer unknown - KUQ/Future unknowns - Recall: leaderboard

AbstentionBench evaluates whether a model appropriately declines unanswerable or underspecified questions. This slice reports answer unknown - KUQ/Future unknowns - Recall as Abstention Recall (%); values converted from 0-1 to percent. Precision and recall must be read together: always abstaining is not necessarily useful. Scenario/dataset pairs and SFT/DPO/RL checkpoints remain separate; no synthetic overall average. Source: facebookresearch/AbstentionBench analysis/abstention_performance.csv.

Metric: Abstention Recall (%). Source: github.com. Saturation forecast: Estimated already saturated. 20 models tracked.

Top models

#ModelScoreOverall rank
1Llama-3.1-Tulu-3-70B-DPO81.6#732
2Llama-3.1-Tulu-3-70B80.06#499
3Gemini 1.5 Pro75.77#454
4Llama-3.1-Tulu-3-8B-DPO73.62#992
5Llama-3.1-Tulu-3-8B73.31#848
6GPT-4o71.17#357
7Llama 3.1 8B Instruct69.94#1017
8Llama 3.3 70B Instruct69.94#541
9Mistral 7B Instruct (v0.3)68.4#1255
10Qwen 2.5 32B Instruct66.56#509
11O166.56#190
12Llama 3.1 405B Instruct65.34#470
13OLMo-7B-0724-Instruct-hf64.11#1441
14Llama-3.1-Tulu-3-8B-SFT62.58#977
15Llama 3.1 70B Instruct62.27#567

No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.

Interactive version: theaggregate.ai/benchmark?slug=abstentionbench-answer-unknown-kuq-future-unknowns-recall · How It Works · Data refreshed daily, snapshot 2026-11-05.